Example-based support vector machine for drug concentration analysis

Abstract

Abstract-Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (SVM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVMbased approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

TAT-based formal representation of medical guidelines: Imatinib case-study

“…There exist several models of the patient body reaction reflecting only such a specific aspect as drug concentration in the blood among which we can name the pharmakokinetical models [20] or the SVM-based approach [21]. The Imatinib protocol presented above can not benefit from these models.…”

Medical guidelines reconciling medical software and electronic devices: Imatinib case-study

“…However, they reflect only a specific aspect of drug concentration prediction in the blood. Among them we can name the pharmakokinetical models [13] and the SVM-based approach [29].…”

Safe Implementation of Embedded Software for a Portable Device Supporting Drug Administration

“…Several personalized drug concentration prediction method based on Support Vector Machine (SVM) algorithm where presented in our prior works [20]- [22]. The initial method was only able to perform a point-wise drug concentration prediction, therefore, it is impossible to calibrate in personalized manner the prediction every time when a new measured concentration value is available for the patient under treatment.…”

“…A Gaussian Kernel is applied in a similar way as in [20]. Therefore, the prediction function becomes:…”

“…Unlike in [20] and [26] where the SVM algorithm was used to predict the drug concentration values based on patient parameters, here the SVM algorithm is applied to learn the mathematical relationship between the parameters of the basis functions and then to predict the parameter values of the DCT curve for a new patient in the testing dataset. With one given measured concentration value, in case of a posteriori adaptation, or a value of the residual concentration after the previous dose intake, the curve parameters can be adjusted, this way allowing to build the personalized DCT curve for each next cycle.…”